Advanced ensemble learning strategy based semi-supervised soft sensing method

The present disclosure provides a novel advanced ensemble learning strategy for soft sensor development with semi-supervised model. The main target of the soft sensor is to improve the prediction performance with a limited number of labeled data samples, under the ensemble learning framework. Firstl...

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Bibliographic Details
Main Authors Che, Xiaoqing, Xiong, Weili, Shi, Xudong, Sheng, Xiaochen, Gu, Bingbin
Format Patent
LanguageEnglish
Published 01.11.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:The present disclosure provides a novel advanced ensemble learning strategy for soft sensor development with semi-supervised model. The main target of the soft sensor is to improve the prediction performance with a limited number of labeled data samples, under the ensemble learning framework. Firstly, in order to improve the prediction accuracy of sub-models for ensemble modeling, a novel sample selection mechanism is established to select the most significantly estimated data samples. Secondly, the Bagging method is employed to both of the labeled and selected data-set, and the two different kinds of datasets are matched based on the Dissimilarity (DISSIM) algorithm. As a result, the proposed method guarantees the diversity and accuracy of the sub-models which are two significant issues of the ensemble learning. In this work, the soft sensor is constructed upon the Gaussian Process Regression (GPR) model.
Bibliography:Application Number: US202016837428